Case-based reasoning

INFO 612 Dr. R. Weber

Where does it come from?

• Pursuit of representation of memory: Schank’s Dynamic Memory (1982) concept (i.e. scripts, MOPS, links ) • role of understanding in solving problems • similarity heuristic • the reminding of a past episode that is similar to a current one so that one can apply a strategy/solution that has worked in a similar episode

CBR assumptions hypotheses

• similar problems have similar solutions • problems recur (Leake, 1996)

Definitions

• From Riesbeck & Schank (1989), "A case-based

reasoner solves new problems by adapting solutions that were used to solve old problems".

• Case-Based Reasoning systems mimic the

human act of reminding a previous episode to solve a given problem due to the recognition of their affinities.

• Case-based reasoning is a methodology that

reuses previous episodes to approach new situations. When faced with a new situation, the goal is to retrieve a similar previous one and reuse its strategy .

CBR and AI tasks

  Mundane • prediction-advice  composition  understanding  reading  planning  walking  uncertainty  creativity Both  interpretation  classification  categorization  discovery  control  monitoring  learning  planning  analysis  explanation • Expert  diagnosis troubleshooting  prescription  configuration  design  scheduling  retrieval  mediation  argumentation  recommendation

CBR applications

•deployed •emerging •research

Deployed

CBR applications CCBR conversational CBR e gain

Examples from Lucas Arts

• • PROFIT valuates residential properties to evaluate mortgage packages for a division of GE Mortgages. Values of a property change with market conditions, so estimates have to be updated constantly according to real estate transactions, which validate the estimations. CARMA is designed to provide expert advice on handling rangeland grasshopper infestations. CARMA has reused its expertise combined with model-based methods to devise policies on pest management and the development of industry strategies.

• General Motors has developed an organizational CBR system to support the goals of dimensional management, an area in the manufacturing of mechanical structures (e.g., vehicle bodies) that enforces quality control by reducing manufacturing variations that occur in fractions of millimeters. • Western Air is an Australian distributor of heat and air conditioning systems; they have chosen to use a web-based CBR application [20] to guarantee a competitive advantage that also poses an entry barrier to competition. They guarantee the precision of the specifications of each new system and the accuracy of the quotes by relying in knowledge captured in previous installations.

PTV combines case-based (content based) personalization with collaborative filtering to recommend shows to watch on digital television.

NEC has developed SignFinder, which is a system that detects variations in the case bases generated automatically from customer calls. When they detect variations on the content of typical customers requests, they can discover knowledge about defects on their products faster than with any other method.

PTV (personalized TV listings) ALFA Each user receives a daily personalized TV listing specially compiled to suit each user’s individual preferences Predict power demand Cotter & Smyth Jabour Cbr and collaborative filtering CF makes a recommendation to a person because his or her profile is similar to other people who have chosen the recommended item.

Same result but faster than human experts Compaq SMART HVAC system Diagnosis and repair; customer support help desks Tests and diagnosis of faults in A/C systems CLAVIER Design and evaluation of autoclave loading Acorn, Walden Watson, 2000 Barletta & Hennessy Uses Inference ’ s tool; can be used by up to 60 users at a time; shows that library engineering is necessary Diagnosis maintenance and solutions to HVAC Operated by salespersons Western Australia Interacts planning and scheduling SQUAD Software quality control advisor Kitano 20,000 cases in 1993 FormTool CBR in color matching Cheetham GE CRD Savings of 2.25 million per year in productivity and cost reduction

Emerging

• Dublet recommends apartments for rental in Dublin, Ireland, based on a description of the user’s preferences. It employs information extraction from the web (of apartments for rent) to create cases dynamically and retrieves units that match the user’s preference. Dublet performs knowledge synthesis (creation) and extends the power of knowledge distribution of the CBR system by being operational in cell phones.

The Auguste Project CBR is used to decide whether a patient benefits from a decides choose drug which and drug RBR to Marling 2001 HICAP Planning ongoing care for AD (Alzheimer) cases based on strategies that worked better in past cases DUBLET Recommends rental properties from different online sources Hurley, Wilson 2001 Is used on the web and in mobile phones Employs Information Extraction tools to gather info from the web- returns properties ranked according to similarity

Research

name ABBY ARCHIE ARCHIE 2 CADET CASEY task Romantic advisor; retrieves a similar history Architecture design of office buildings Design of mechanical components Diagnosis cause and prescribes solution to heart problems author Goel, Sycara, Navinchandra Koton obs.

and Domschek Kolodner Abstract indexing allowed innovative design model-based CHEF Design of recipes to meet different simultaneous goals Hammond case-based planning: Memory started with 20 recipes and learned from user feedback COACH HYPO JUDGE JULIA Planning soccer games Interpretation and argumentation Defines sentences of delinquent crimes based on the chances of repeating the crime and its severity planning meals Collins Rissland & Ashley Bain Hinrichs Debugging and fixing bad strategies; memory keeps strategies and the type of problem Retrieves similar cases to create a point, a response, and a rebuttal using hypotheticals (Ashley, 1990) In case of not having a sufficient similar case, the system uses heuristics to determine the sentence Plausible reasoning and design

name CYRUS AQUA CASCADE ASK CELIA task author stored and retrieved events in the life of Cyrus Vance when he was secretary of Kolodner Ram explanation on terrorism assistance on recovering from crashes in VMS OS user directed exploration of stories and guidelines describing a task or domain automated diagnosis and interactive learning; predicts an expert’s action and relate steps Simoudis & Miller Ferguson, Bareiss, Schank Redmond obs.

first implementation of MOPs reconstructive dynamic memory reads newspaper stories and asks questions, learning through incremental revision of knowledge; case-based explanation (first) help desks; emphasis on efficient retrieval when first descriptions are not rich ASK Tom trust bank consulting; ASK Michael industrial dvlpmnt acquiring cases, learning indexes, combines cbr and other methods Mostly from Kolodner 1993

name task MEDIATO R PERSUADE R AMADEUS PLEXUS Mediates conflicts by performing planning Mediation of union negotiations; proposes solutions with arguments suggests how to write papers Planning daily tasks PRODIGY Planning and learning PROTOS Heuristic classification for diagnosis author Simpson Sycara obs.

Keeps in memory failed solutions and tries to avoid same failures in new solutions Considers part’s goals and considers recent accepted solutions Aluisio, 1995 Alterman Veloso, Carbonell Bareiss, Porter, Murray, Weir, Holte Adapts the experience of riding the SF metro to reuse in NY Demonstrated in a variety of domains Automatic knowledge acquisition; good for weak theory domains SWALE Generates explanation of anomalous events in news stories Schank, Kass, Leake, Owens Mostly from Kolodner 1993 Searches for similar explanations for death and destruction such as the murdered spouse that was killed because of the insurance money just like the horse (SWALE) that was killed by its owner for the same reason

name CATO task Tutoring system author Aleven/Ashle y obs.

Teaching law students to create argument HICAP PRUDENTIA Case-based planning Jurisprudence textual CBR research; Munoz 1999 Avila Weber, 1998 Combines case-based methods in planning NEO’s planning Case retrieval with Recent applications Springer series on CBR Research and Development

CBR systems types

• interpretive: – past cases are used as references to categorize and classify new cases – interpretation, diagnosis • problem-solving: – past cases are used to provide a solution to be applied to new cases – design, planning, explanation, lessons learned

Task?

case representation

CBR methodology

case base

CBR methodology

situation assessment case base

CBR methodology

case base

CBR development life-cycle

• Knowledge acquisition • CBR design and implementation – Situation assessment – Retrieve – Revise – Review – Retain • Validate • Maintain

Knowledge acquisition

• From humans • From data • From databases • From text: textual CBR • Machine learning (e.g., data mining to learn cases)

CBR design and implementation

• Situation assessment • Retrieve • Revise • Review • Retain • (Validation) • Maintenance design

Design decisions in CBR systems (i)

Which are the cases?

What is the task?

How will the case base be organized?

case base

How will the cases be represented?

Which will be the indexing vocabulary?

What is the task?

How will the case base be organized?

Design decisions in CBR systems (ii)

How will new cases be input?

How to perform retrieval?

Identify features Initially match (similarity assessment) Search Select

input problem Retrieval initial solutions

Design decisions in CBR systems (iii)

How to implement reuse?

From Select or with a combination?

How to display the proposed solution?

initial solutions Reuse proposed solution

Design decisions in CBR systems (iv)

Is the proposed solution good?

How to determine and find what to adapt?

Where is adaptation knowledge?

confirmed solution

case repair

Revise proposed solution

case adaptation

Design decisions in CBR systems (ii)

Is it the type of task that it is worth learning?

Index new case before retain.

Retain.

Retain confirmed solution case base

• Knowledge in case-based reasoning systems

• by Richter, Knowledge Similarity M.

M., “The Contained Measures: in Some remarks on the invited talk given at ICCBR'95 in Sesimbra, Portugal, October 25, 1995 ”.

Online: http://www.cbr web.org/documents/Richtericc br95remarks.html

validation & verification (i)

• Validation refers to establishing the effectiveness of a system in light of its intended purposes • Verification indicates how correct a given system can solve its proposed tasks (Watson) • Retrieval accuracy is indicated by the result given by the system when the target case is part of the case collection.

validation & verification (ii)

• Retrieval consistency: the same retrieval when executed the second time must retrieve exactly the same cases (e.g., with the same similarity if k NN is used) • Case Duplication: when two distinct cases receive the same value for similarity in relation to a given target case. • When the same value is attributed to different cases the user or the system has to decide which one to use by evaluating the value for each attribute. The same measure of similarity does not mean the cases necessarily teach the same lessons.

validation & verification (iii)

• Case Coverage is checked for the evenly distribution of cases when they are manipulated and not actual experiences that are collected as they happen.

• Efficiency verification : comparison to alternative methods, empirical evaluations • Retrieval time • Retrieval sorting • Case base consistency can be indicated by retrievals resulting cases with gradual values of similarity. A retrieval that no case has a high value of similarity or too many cases have the same value suggests inconsistency in the case base

maintenance

• if the reasoner learns, the maintenance is more elaborate • statistics of case usage • perform validation tests continuously • special issue on case-based maintenance • Neural networks and other soft computing methods have been proposed • methods for distributed case bases

Advantages of CBR systems (i)

Knowledge acquisition and representation

: There is no need to explicit acquire and represent all the knowledge the system can use.

Common sense:

knowledge that would have to be represented explicitly is implicitly stated in cases.

Not easily formalizable tasks

: such as in some medical domains, prototypical descriptions represent more easily a body of knowledge.

Creativity

- Case solutions can be combined into new ones and cases can also be used in a different level of abstraction providing innovative solutions.

Advantages of CBR systems (ii)

CBR systems can avoid mistakes Learning

- can be done without human interference; CBR systems can become robust and provide better solutions. User’s feedback is easily incorporated in the revise phase.

Degradation

-CBR systems can recognize when no answer exists to a problem by simply defining a threshold from which a solution is no longer acceptable. In decomposable problem domains, a solution can be created from the combination of partial solutions.

(shared with ES and other AI) Permanence

forget.

- CBR do not

Breadth

- One CBR system can entail knowledge learned from an unlimited number of human experts.

Reproducibility

- Many copies of a CBR system.

books, resources

• Leake, D. (1996). Case-Based Reasoning: Experiences, Lessons, and Future Directions. AAAI Press/The MIT Press, Menlo Park, California, 1996.

• Kolodner, J. (1993). Case-Based Reasoning. Morgan Kaufmann, Los Altos, CA.

• Watson, Ian (1997). Applying Case-Based Reasoning: techniques for enterprise systems. Morgan Kaufmann Publishers, Inc. San Francisco, California.

• Schank, R. (1982). Dynamic Memory: A theory of learning in computers and people. New York, Cambridge University Press.

• Schank, R., Kass, A. and Riesbeck, C. (1994). Inside case-based explanation. Lawrence Erlbaum Assoc., Hillsdale, N.J.

• Ashley, Kevin D. (1990). Modeling Legal Argument: reasoning with cases and hypotheticals. A Bradford book. The MIT Press, Cambridge, Massachussetts.

• Lecture Notes in Computer Science, CBR research and development, Springer • Lecture Notes in Computer Science, Advances in CBR, Springer • ai-cbr.org

Building (shells), using,

• Shells/tools

maintaining

– http://www.cbr-web.org/CBR-Web/?info=tools&menu=pt – Esteem examples, NISTP CBR Shell examples Using – Laypeople, experts • Maintaining – Automatically learning new cases • Cases are real or created – Manually adding new cases

Further reading

• Riesbeck & Schank (1989) Inside case-

based reasoning

• Kolodner (1993) Case-based reasoning • Aamodt & Plaza (1994) AICom paper • Leake (1996) Leake, David. (1996).

Case-Based Reasoning: Experiences,

Lessons, and Future Directions. • Watson (1997) Applying Case-Based

Reasoning: techniques for enterprise

systems.

Yes* Use the mode Do the first 5 cases have the same similarity?

No Do the first 4 cases have the same similarity?

Yes** No Yes Use result from the 5th.

Is there conflict?

*No conflict possible; ** Conflict possible No Use the mode Yes Use result from the 3rd Yes* Do the first 3 cases have the same similarity?

No Use the mode Yes** Do the first 2 cases have the same similarity?

No Is there conflict?

No Use their result Use the result from the most similar